class_docstr: Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
  this superclass for more information regarding those methods.
  
  Args:
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      extra_ids (`int`, *optional*, defaults to 125):
          Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
          accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
          indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
          like in ByT5 preprocessing see
          [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
      additional_special_tokens (`List[str]`, *optional*):
          Additional special tokens used by the tokenizer.
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docstr: Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not
  make use of token type ids, therefore a list of zeros is returned.
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  
  Returns:
      `List[int]`: List of zeros.
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code: def create_token_type_ids_from_sequences(
      self,
      token_ids_0: List[int],
      token_ids_1: Optional[List[int]] = None,
  ) -> List[int]:
      """
      Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not
      make use of token type ids, therefore a list of zeros is returned.
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
  
      Returns:
          `List[int]`: List of zeros.
      """
      eos = [self.eos_token_id]
  
      if token_ids_1 is None:
          return len(token_ids_0 + eos) * [0]
      return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
  
file_path: transformers/src/transformers/models/byt5/tokenization_byt5.py
imports: import warnings
  from typing import List, Optional, Tuple
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 6 for calling the ByT5Tokenizer obj's underlying member method code with example usage: obj.create_token_type_ids_from_sequences(token_ids_0, token_ids_1) and returns: unknown, unknown